Exploration vs. Data Refinement via Multiple Mobile Sensors

Clicks: 238
ID: 118253
2019
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Abstract
We examine the deployment of multiple mobile sensors to explore an unknown region to map regions containing concentration of a physical quantity such as heat, electron density, and so on. The exploration trades off between two desiderata: to continue taking data in a region known to contain the quantity of interest with the intent of refining the measurements vs. taking data in unobserved areas to attempt to discover new regions where the quantity may exist. Making reasonable and practical decisions to simultaneously fulfill both goals of exploration and data refinement seem to be hard and contradictory. For this purpose, we propose a general framework that makes value-laden decisions for the trajectory of mobile sensors. The framework employs a Gaussian process regression model to predict the distribution of the physical quantity of interest at unseen locations. Then, the decision-making on the trajectories of sensors is performed using an epistemic utility controller. An example is provided to illustrate the merit and applicability of the proposed framework.
Reference Key
gunther2019entropyexploration Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Mohammad Shekaramiz,Todd K. Moon,Jacob H. Gunther;Mohammad Shekaramiz;Todd K. Moon;Jacob H. Gunther;
Journal entropy
Year 2019
DOI
10.3390/e21060568
URL
https://sciprofiles.com/publication/view/1d62a1420cae598fbaf9c27072681459
Keywords
exploration mobile sensors sensor configuration adaptive sampling data refinement epistemic utility controller gaussian process regression (gpr) decision under conflict

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